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Vergleichen Sie biologische Modelle, Docking-Engines und Berechnungs-Workflows Seite an Seite. Filtern Sie nach Zugangsmodell, Rechenbedarf und Kategorie, um in unter einer Minute zu einer verwertbaren Entscheidungstabelle zu gelangen.
| Attribut | ＋ Werkzeug hinzufügen | |||
|---|---|---|---|---|
| Category | Biological AI Models | Biological AI Models | Biological AI Models | — |
| Subcategory | Protein Structure | Protein Design | Protein Design | — |
| Company | AlQuraishi Laboratory / OpenFold contributors | Pacesa Lab | Boltz Team | — |
| Access mode | Public code / project terms | Research code or publication | Research code or publication | — |
| Licence conditions | Review the linked release, model weights, datasets and service terms separately | Check code and model weights separately | Check code and model weights separately | — |
| Compute requirements | Consult linked installation guidance; workload dependent | Check selected checkpoint and runtime; no GPU catalog recommendation | Check selected checkpoint and runtime; no GPU catalog recommendation | — |
| Biological application | Nicht erfasst | Nicht erfasst | Nicht erfasst | — |
| Research workflow | Nicht erfasst | Nicht erfasst | Nicht erfasst | — |
| Evidence level | Project or provider documentation; no local performance benchmark | Peer-reviewed primary method | Primary research preprint | — |
| Commercial eligibility | Not independently confirmed | Unknown, requires audit | Unknown, requires audit | — |
| Price | See project terms; compute costs may apply | Check selected model licence | Check selected model licence | — |
| Availability ⓘ | See project access and maintenance status | Public research method/code | Public research method/code | — |
| Features | Trainable PyTorch implementation of the AlphaFold 2 approach, Input: Protein sequences, alignments and training or inference configuration, Output: Protein structures or trained folding models | AF2-guided optimization pipeline proposes protein binder designs for target structures., Candidate binder sequences, modeled protein complexes | Target-conditioned protein and peptide binder generation model as reported in its preprint., Binder sequence and structure candidates | — |
| Entry type | Research software / model | Research AI model / method | Research AI model / method | — |
| AI role | Scientific machine learning | Published biomedical AI method | Published biomedical AI method | — |
| Input data | Protein sequences, alignments and training or inference configuration | Target protein 3D coordinates; AF2/MPNN workflow and target interface constraints | Target context; design constraints | — |
| Output data | Protein structures or trained folding models | Candidate binder sequences; modeled protein complexes | Binder sequence and structure candidates | — |
| Validation | Evaluate on representative data; no clinical suitability established | Requires independent dataset-specific evaluation | Requires independent dataset-specific evaluation | — |
| Type | Nicht erfasst | Nicht erfasst | Nicht erfasst | — |
| Intended use | Biological and pharmaceutical research | Nicht erfasst | Nicht erfasst | — |
| Compatibility | Nicht erfasst | Nicht erfasst | Nicht erfasst | — |
| Limitations | Training and inference have different resource needs; code and downloaded parameters have separate terms. | Nicht erfasst | Nicht erfasst | — |